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Near-real-time demand side management of battery-electric vehicle charging stations in residential complexes of Vorarlberg using Methods of linear and stochastic optimization

  • The number of electric vehicles will increase rapidly in the coming years. Studies suggest that most owners prefer to charge their electric vehicle at home, which will fuel the need for charging stations in residential complexes where vehicles can be charged overnight. Currently, there already are over 100 such residential complexes, with another 70 added every year in Vorarlberg alone. In most existing residential complexes, however, the grid connections are not sufficient to charge all vehicles at the same time with maximum power. In addition, it is also desirable for grid operators and electricity producers that the power demand be as smooth and predictable as possible. To achieve this, ways to manage flexible loads need to be found, which can operate within the technical constraints. Therefore, the most common scenarios how the load can be made grid-friendly with the help of optional battery storage and/or photovoltaics using optimization methods of linear and stochastic programming were examined. At the same time, the needs of the vehicle owners for charging comfort - namely to find their vehicles reliably charged at the time of their respective departure - were addressed by combining both objectives using suitable weights. The algorithms determined were verified in practice on an existing Vlotte prototype installation. For this purpose, the necessary programs were implemented in Python, so that the data obtained during the test operation, which lasted one month, could be subjected to a well-founded analysis. In addition, simulation studies helped to further reveal the influence of PV and BESS sizing on the achievable optimums and confirm that advanced optimization algorithms such as the ones discussed are a vital contribution in reducing the charging stations’ peak load while at the same time maintaining high satisfaction levels.

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Metadaten
Author:Albert Ulmer
DOI:https://doi.org/10.25924/opus-4563
Title Additional (German):Quasi-Echtzeit Lastmanagement von Ladestationen für elektrische Fahrzeuge in Wohnanlagen Vorarlbergs mit Methoden der linearen und stochastischen Optimierung
Advisor:Klaus Rheinberger
Document Type:Master's Thesis
Language:English
Year of publication:2022
Publishing Institution:FH Vorarlberg (Fachhochschule Vorarlberg)
Granting Institution:FH Vorarlberg (Fachhochschule Vorarlberg)
Release Date:2022/09/13
Tag:Demand Side Management; Linear Optimization; Peak Shaving; Stochastic Optimization
Number of pages:VI, 80
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
JEL-Classification:C Mathematical and Quantitative Methods
Open Access?:ja
Course of Studies:Nachhaltige Energiesysteme
Licence (German):License LogoUrhG - The Austrian Copyright Act applies - Es gilt das österr. Urheberrechtsgesetz